WO2015157186A1 - Procédés et systèmes améliorés pour modéliser des formations géologiques - Google Patents

Procédés et systèmes améliorés pour modéliser des formations géologiques Download PDF

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Publication number
WO2015157186A1
WO2015157186A1 PCT/US2015/024546 US2015024546W WO2015157186A1 WO 2015157186 A1 WO2015157186 A1 WO 2015157186A1 US 2015024546 W US2015024546 W US 2015024546W WO 2015157186 A1 WO2015157186 A1 WO 2015157186A1
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Prior art keywords
region
child
parent
grid
geological model
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PCT/US2015/024546
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English (en)
Inventor
Douglas A. Palkowsky
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Hess Corporation
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Priority to US14/777,539 priority Critical patent/US20160292320A1/en
Publication of WO2015157186A1 publication Critical patent/WO2015157186A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
    • G06T17/005—Tree description, e.g. octree, quadtree
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
    • G06T17/05—Geographic models
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V20/00—Geomodelling in general
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2210/00—Indexing scheme for image generation or computer graphics
    • G06T2210/36—Level of detail

Definitions

  • the present disclosure relates generally to modelling geological formations and more particularly, to improved methods and systems for efficiently and accurately modelling geological formations.
  • geological formations may be located onshore and/or offshore. For instance, in order to efficiently retrieve natural resources such as hydrocarbons from geological formations it is desirable to be able to understand the structure, rock, and fluid properties of such formations. Similarly, retrieval of other natural resources often requires an accurate understanding of the geological formation where such resources are located.
  • geological modelling of a formation refers to creating a computerized representation of the portions of the earth's crust that form the formation based on geophysical and geological observations that may be made on and/or below the earth's surface.
  • Current approaches for developing geological models have several draw backs. Specifically, there are a number of situations where it may be desirable to be able to selectively divide a region of interest into smaller regions, manipulate the smaller regions and/or integrate the smaller regions back together to assemble an accurate model for the region as a whole.
  • Figure 1 depicts an illustrative geological area of interest (AOI) to be modeled.
  • the entire region to be modeled may be large in size.
  • the region of interest (denoted as 100) may consist of a plurality of smaller regions of interest (denoted as 102A-G).
  • the region of interest 100 as a whole may be referred to as the parent region and each of the smaller regions of interest 102A-G within the parent region of interest may be referred to as a child region.
  • modelling the parent region as a whole may be problematic.
  • modelling such a large area will require the generation of geocellular grids consisting of millions of cells which can be time and resource intensive. Moreover, populating rock and fluid properties for each of these cells contained in such a large model can also be slow and resource intensive. It is unlikely that the same user or users would be interested in constructing and analyzing the whole parent region 100. Instead, it is more likely that different users or teams of users will be responsible for analyzing the different child regions 102A-G or different groups of child regions 102A-G. It is undesirable for each user or group of users to have to load and manipulate data relating to all the cells in the parent region 100 when they are only interested in one or two of the child regions 102A-G.
  • Figure 2 depicts another illustrative application where is desirable to be able to integrate models for multiple child regions.
  • a parent region 200 may consist of multiple child regions 202A, 202B, and 202C.
  • a first team may have developed a geological model for the child regions 202A and 202C and a second team may have developed a geological model for the child region 202B.
  • the child regions 202A, 202B, and 202C are located adjacent to one another and likely interact.
  • an integrated model for the parent region 200 will be more useful than the three distinct models developed for the child regions 202A, 202B, and 202C.
  • Figure 3 depicts another illustrative application where it may be desirable to integrate independently developed geological models of multiple child regions.
  • a geological model for a first region 302 or Area of Interest (“AOI") may be initially developed. It may then become necessary to expand the AOI for the first region 302. For instance, it is possible that specific analysis of the formation of interest may require information about the characteristics of rock formation surrounding region 302. Accordingly, the user may then decide to expand the model to include the rocks located above (over burden), below (under burden) and to the sides (side burden) of the first child region 302. This larger rock region is denoted as a second region 304 in Figure 3. Since a model for the first region 302 exists, it would be undesirable to require the user to recreate that geological model.
  • AOI Area of Interest
  • the first region 302 becomes a child to the parent region 304.
  • the child-parent region model is created from the child by embedding the existing first region into a larger parent region.
  • the Petrel ® E&P Software Platform available from Schlumberger, Inc. provides the user with some capabilities for extracting a child region from a larger parent model.
  • Formal hierarchical child models can be created using a technique referred to as local grid refinement (LGR).
  • LGR local grid refinement
  • This technique is common for finite difference fluid flow simulation software.
  • LGR local grid refinement
  • a locally refined grid model can only inherit property values from its parent global grid model.
  • Such an LGR cannot be extracted for subsequent manipulation and integrated later on.
  • existing global refinement methods produce a single child grid model at a finer resolution that covers the entire AOI of the parent model. Integrating such a refined grid model requires an "upscaling" step.
  • Petrel ® the parent region is the "active" component which stores the information relating to the location of its grid cells. Accordingly, in order to incorporate a child region back into a parent region Petrel ® needs to query each cell in the parent region model and determine which cells in the child model correspond to the given parent cell. This is a time consuming and resource intensive process, especially in instances where the parent region is large in size and potentially covers a much larger AOI.
  • Figure 1 is a first illustrative example of a parent region having a plurality of child regions.
  • Figure 2 is a second illustrative example of a parent region having a plurality of child regions.
  • Figure 3 is a third illustrative example of a parent region having a plurality of child regions.
  • Figures 4A-4C depict the implementation of a typical up-scaling process in accordance with the prior art.
  • Figures 5A-5C depict the creation of a grid compatible fine grid from a coarse grid in accordance with an illustrative embodiment of the present disclosure.
  • Figure 6 depicts the method steps in accordance with an illustrative embodiment of the present disclosure.
  • Figure 7A depicts the selection of a child region from a parent region for further analysis in accordance with an illustrative embodiment of the present disclosure.
  • Figure 7B depicts the extraction of a child model and a grand-child model from a parent model in accordance with an illustrative embodiment of the present disclosure.
  • Figure 7C depicts the implementation of a fast index approach in accordance with an illustrative embodiment of the present disclosure.
  • the present disclosure relates generally to modelling geological formations and more particularly, to improved methods and systems for efficiently and accurately modelling geological formations.
  • an information handling system may include any instrumentality or aggregate of instrumentalities operable to compute, classify, process, transmit, receive, retrieve, originate, switch, store, display, manifest, detect, record, reproduce, handle, or utilize any form of information, intelligence, or data for business, scientific, control, or other purposes.
  • an information handling system may be a personal computer, a network storage device, or any other suitable device and may vary in size, shape, performance, functionality, and price.
  • the information handling system may include random access memory (RAM), one or more processing resources such as a central processing unit (CPU) or hardware or software control logic, ROM, and/or other types of nonvolatile memory.
  • Additional components of the information handling system may include one or more disk drives, one or more network ports for communication with external devices as well as various input and output (I/O) devices, such as a keyboard, a mouse, and a video display.
  • the information handling system may also include one or more buses operable to transmit communications between the various hardware components. It may also include one or more interface units capable of transmitting one or more signals to a controller, actuator, or like device.
  • Computer-readable media may include any instrumentality or aggregation of instrumentalities that may retain data and/or instructions for a period of time.
  • Computer-readable media may include, for example, without limitation, storage media such as a direct access storage device (e.g., a hard disk drive or floppy disk drive), a sequential access storage device (e.g., a tape disk drive), compact disk, CD-ROM, DVD, RAM, ROM, electrically erasable programmable read-only memory (EEPROM), and/or flash memory; as well as communications media such as wires, optical fibers, and/or optical carriers; and/or any combination of the foregoing.
  • direct access storage device e.g., a hard disk drive or floppy disk drive
  • sequential access storage device e.g., a tape disk drive
  • compact disk CD-ROM, DVD, RAM, ROM, electrically erasable programmable read-only memory (EEPROM), and/or flash memory
  • communications media such as wires, optical fibers, and/or
  • Couple or “couples” as used herein are intended to mean either an indirect or a direct connection.
  • a first device couples to a second device, that connection may be through a direct connection or through an indirect mechanical or electrical connection via other devices and connections.
  • communicately coupled as used herein is intended to mean either a direct or an indirect communication connection.
  • Such connection may be a wired or wireless connection such as, for example, Ethernet or LAN.
  • wired and wireless connections are well known to those of ordinary skill in the art and will therefore not be discussed in detail herein.
  • a first device communicatively couples to a second device, that connection may be through a direct connection, or through an indirect communication connection via other devices and connections.
  • parent region refers to an area of interest (AOI) which may itself include a plurality of smaller AOIs that are each referred to as a "child region.”
  • the parent region and/or the child region are not limited to any specific size or range of sizes and may be different in size depending on the particular application.
  • parent model and child model as used herein generally refer to a geological model of a parent region and the geological model of a child region, respectively.
  • grid compatibility refers to the correlation between the alignment of cells in a coarser grid as compared to a finer grid corresponding to the same AOI.
  • up-scaling refers to the process of resampling a finer geological model having a higher resolution onto a coarser geological model having a lower resolution.
  • a first geological model may comprise of 100,000 cells. It may be desirable to create a second, more coarse geological model with 10,000 cells. Accordingly, the first geological model may be resampled and "up- scaled" to create the second geological model. This is illustrated and discussed in further detail in conjunction with Figures 4A-4C below.
  • the methods disclosed herein facilitate grid compatibility when sampling, upscaling, or downscaling between grids and provide for access rules to manage user operations on the plurality of child regions that make up a parent region.
  • Figures 4A-C depict the implementation of a typical up-scaling process in accordance with the prior art.
  • the typical process entails starting with a fine grid and sampling that fine grid onto a coarser grid.
  • Figure 4A depicts an AOI having a fine grid. It may be desirable to up-scale the AOI of Figure 4A into a more coarse grid as shown in Figure 4B.
  • Figure 4B depicts a coarse grid superimposed onto the fine grid of Figure 4A.
  • Figure 4C depicts a close up view of the cells of a selected region of Figure 4B and illustrates the relationship between the cells of the fine grid of Figure 4A relative to the coarse grid of Figure 4B.
  • typical up-scaling procedures do not yield grid compatibility.
  • the corners of the cells of the fine grid (the cells drawn with solid lines) do not coincide with the comers of the cells of the coarse grid (the cells drawn with dashed lines).
  • the cell intersections for each cell must be recalculated when up- scaling the model.
  • the typical up-scaling process shown in Figures 4A-C assumes a reasonably common AOI.
  • the typical process of translating between a fine grid and a coarse grid utilizes the fine grid as the source grid and the coarse grid as the target grid and is target centric.
  • the coarse grid i.e., the target grid
  • the system loops over each cell in the target grid and for each cell, the system searches all the cells in the source grid to identify the cells of the source grid that correspond to the particular cell in the target grid and occupy the same spatial region.
  • the system loops over and searches all the cells in the source grid to identify what may be a very small subset of those cells that correspond to the particular cell in the target grid.
  • the source grid may be large and may include millions of cells making this process highly inefficient.
  • the system must then subdivide the cells of the source grid as necessary to obtain partial cell volume weights in order to populate data in the cells of the target grid. To that end, the system computes the effective property for each target grid cell based on the source grid cells as obtained using the partial cell volume weights to account for grid incompatibility.
  • the effective property for each target grid cell may be determined, for example, using a weighted average such as arithmetic mean, harmonic mean, geometric mean, or flow-based tensor values of the corresponding source grid cells.
  • This process utilizes significant system resources such as, for example, memory and CPU time.
  • the lack of grid compatibility leads to inaccurate results and sampling problems when translating the geological model as shown in Figures 4A-C.
  • any boundary conditions in the finer grid might be lost due to this grid incompatibility.
  • the methods and systems disclosed herein eliminate two main disadvantages of the traditional methods discussed above.
  • the methods and systems disclosed herein eliminate the need for calculating values for cell intersections which result from grid incompatibility between a fine grid and its corresponding coarse grid without sacrificing accuracy.
  • the methods and systems disclosed herein eliminate the expenditure of system resources to loop through and search the cells of a target grid in order to identify the cells of the source grid that occupy the same spatial locations as each particular cell of the target grid.
  • the methods and systems disclosed herein ensure grid compatibility which prevents an intersection of cells of a fine grid with those of a coarse grid. Once grid compatibility is in place, a fast index approach is used to eliminate the need for a target grid to loop through all its cells and identify the cells of a source grid corresponding to each of its cells.
  • a geological model for a parent region is first developed.
  • the parent region may be a large area comprised of a plurality of child regions such as those examples illustrated and discussed in conjunction with Figures 1 -3.
  • any portion of the parent region may be used as the AOI which can be resampled as discussed in further detail below.
  • One or more child regions may then be extracted from the parent region.
  • One or more users may then refine and/or manipulate a child region before reintegrating it back into the geological model of the parent region.
  • grid compatibility is maintained when refining any portion of the parent region into a finer grid.
  • the methods and systems disclosed herein are implemented by first creating the coarse grid and using that coarse grid to create a fine grid. This is discussed in further detail in conjunction with Figures 5A-C.
  • Figure 5 A depicts an AOI from a parent region and Figure 5B depicts an enlarged view of a portion of Figure 5A.
  • this AOI represents the boundary of the coarse grid.
  • This coarse grid may then be refined to create a desired fine grid.
  • a user may specify the desired level of refinement.
  • the coarse grid of Figure 5A may be refined by dividing each cell in that grid into smaller cells as shown in Figure 5C.
  • the coarse grid is subdivided to create the fine grid.
  • FIG. 5C depicts the fine grid that results from processing the coarse grid of the parent region as shown in Figure 5B. The user can then manipulate the data associated with the cells of the fine grid of Figure 5C as desired.
  • the up- scaling of the grid compatible fine grid of Figure 5C into the coarse grid of Figure 5A will now be a simpler process because the cells of the coarse grid correspond to a particular number of cells in the fine grid and there are no cell intersections to be analyzed and calculated.
  • the same process may be repeated to achieve even finer grids having higher resolutions.
  • the finer grid may be transferred back into the coarse grid accurately and without having to expend significant system resources to account for the cell intersections that would result from an incompatible grid.
  • the methods and systems disclosed herein eliminate the expenditure of system resources to search for the cells of the source grid that occupy the same spatial locations as each particular cell of the target grid. This is achieved by using a "back tracking" procedure to keep track of the location of each cell of a child region relative to a parent ancestral region as discussed in further detail below.
  • Figure 6 depicts a flow chart of a process in accordance with an illustrative embodiment of the present disclosure.
  • a geological model of the largest and coarsest desirable AOI parent region is developed.
  • the parent region modelled may be similar to one of the parent regions discussed in conjunction with Figures 1 -3.
  • a desired child region may be extracted from the parent region AOL
  • the child region may be any region of interest within the parent region that is selected by a user. For instance, in certain applications involving large acreages such as unconventional hydrocarbon development, the parent region may be large with different teams/users working on different portion of the parent region.
  • each team/user may extract its corresponding child region AOI for refinement and manipulation.
  • Figure 7A depicts an illustrative parent region with a plurality of child regions and how a user may select one of those child regions (e.g., AOI 3) for further analysis.
  • the child model 702 may be refined. Specifically, as shown in Figure 7B, the child model 702 may be extracted from the parent model 704 and converted from a coarse grid to a fine grid. Moreover, if desired, a grand-child model 706 may be extracted from the child model 702 for further manipulation. As shown in Figure 7B, the child model 702 may have a finer grid than the parent model 704 and the grand-child model 706 may have a finer grid than the child model 704. In each instance, the finer grid is created from the coarser grid in the same manner discussed above in conjunction with Figures 5A-5C so that grid compatibility is maintained between the parent 704, the child 702 and the grand-child 706 as shown in Figure 7B.
  • the parent-child relationship is maintained at step 608.
  • a fast index back tracking approach is used to determine the coordinates of each cell in the parent region. This is shown in further detail in Figure 7C.
  • the I, J, and "back tracking" indices with regard to the parent region are determined.
  • the associated indices (hereinafter "fast-indices") for each cell are then stored, specifying the exact spatial location corresponding to that cell in the parent region 704.
  • the associated coordinates for each cell may be stored in a computer readable medium.
  • the fast indices of the cells are determined with respect to any ancestor and stored allowing an almost immediate return to the ancestral parent built at previous levels of refinement. For instance, when going from the parent model 704 to the child model 702 the back tracking fast indices indicating the location of each cell of the child model 702 in the parent model 704 are generated and stored. Accordingly, when the user returns the child model 702 (source grid) to the parent model 704 (target grid) after manipulation and refinement, the target grid 704 need not loop through each of its cells to identify the particular cells of the child model 702 that correspond to each of its cells. Instead, each cell of the child model 702 knows its exact location in the parent model 704 and can directly find that location and update the data value in that cell location in the parent model 704.
  • the fast indices indicating the location of each cell of the grand-child model 706 in the child model 702 are generated and stored. Accordingly, when the user returns the grand-child model 706 (source grid) to the child model 702 (target grid) after manipulation and refinement, the target grid 702 need not loop through each of its cells to identify the particular cells of the grandchild model 706 that correspond to each of its cells. Instead, each cell of the grand-child model 706 knows its exact location in the child model 702 and can directly find that location and update the data value in that cell location in the child model 702.
  • the back tracking fast indices indicating the location of each cell of the child model 702 in the parent model 704 are known. Accordingly, in certain implementations, when going from the child model 702 to the grand-child model 706, the fast indices are also updated and stored to indicate the location of each cell of the grand-child model 706 in the parent model 704. Accordingly, the user can directly return the grand-child model 706 to the parent model 704 and bypass the child model 702. When the user returns the grand-child model 706 (source grid) to the parent model 704 (target grid) after manipulation and refinement, the target grid 704 need not loop through each of its cells to identify the particular cells of the grand-child model 706 that correspond to each of its cells. Instead, each cell of the grand-child model 706 knows its exact location in the parent model 704 and can directly find that location and update that cell location in the parent model 706.
  • the levels of refinement available to a user are not limited to a child and grand-child. In the same manner, a user can generate great-grand-children, etc. from the parent model. In this manner, the methods and systems disclosed herein support a recursive ancestry.
  • step 606 the processes of steps 606 and 608 are repeated.
  • step 612 the child region model can be returned to the parent region model by reintegrating the child model with the parent model.
  • the cells can be returned to their corresponding location in the parent model quickly and efficiently.
  • the child model and the parent model have the same resolution.
  • the exact location of each cell of the child model in the parent model is known using the fast indices as discussed above in conjunction with step 608.
  • the integration of the child model with the parent model is a simple transfer of cell data values.
  • the methods and systems disclosed herein permit a bi-directional transfer of cell data values between the child model and the parent model. Specifically, cell data values may be directed from the parent model to the child model or from the child model to the parent model. Accordingly, the properties (or cell values) of the source grid (child model or parent model) are re-sampled onto the target grid (parent model or child model) using the fast indices which provide the exact location of each cell of the child model in the parent model.
  • step 612 The process implemented in step 612 is different in instances where the child model has been refined and has a higher resolution than the parent model.
  • many cells from the finer child model grid correspond to a single cell from the coarser parent model grid.
  • the data from the child model should be up-scaled when being integrated into the parent model which has a lower resolution and a coarser grid.
  • the exact location of each cell of the child model in the parent model is known using the fast indices as discussed above in conjunction with step 608. Once the single cell in the parent model corresponding to a group of cells in the child model is known, the data values from the group of cells in the child model (“source cells”) may be directed to that particular cell in the parent model ("target cell").
  • any suitable averaging methods known to those of ordinary skill in the art may be used to assign a value to the target cell. For instance, in certain implementations, depending on user preferences, a user may assign the minimum data value, the maximum data value, the mode value, the arithmetic mean value, the geometric mean value, the harmonic mean value, the root mean square or the power mean value of the source cells to the target cell. In certain implementations, a facies bias may be added as an enhancement when directing the data values from the source cells to the target cell.
  • the properties of the source cells in a child model, the back tracking fast indices of the source cells in the child model and a set of user defined transfer parameters may be used to quickly, accurately, and efficiently populate the data in the corresponding target cells in a parent model.
  • a set of user defined transfer parameters e.g., an optional weighting property, an optional bias property and a user defined averaging criteria
  • the parent model should be down-scaled. Such down- scaling is simply a special case of the re-sampling described previously and parent cell values are replicated for each child cell corresponding to a single parent cell.
  • sampling errors during up-scaling/resampling are minimized and a resource efficient process is provided which reduces the required memory and CPU time utilized by the information handling system(s) that are used to implement the disclosed steps.
  • access rules may be developed which: (1) allow only certain users to extract or "check out" child model regions from a larger parent model region; (2) allow only one user at a time to check out and edit a child model region and prevent others from editing that child model region until the user has integrated the changes to the child model region back into the parent model region; and (3) notify other users upon check-out, and once a check out child model region has been checked back in.
  • a unique transaction identifier may be created and a check-out event may be recorded and posted for the said child region.
  • Different users may be notified that said region has been secured for pending manipulation.
  • said child Upon completion of child region manipulation, said child would be returned to the parent at step 612 and a "check in" transaction event may occur against the same unique identifier.
  • Other interested users would be notified of the check-in event.
  • a historical record of all such transactions may be maintained for review and audit purposes.
  • other access rules known to those of ordinary skill in the art may also be implemented without departing from the scope of the present disclosure.
  • the methods disclosed herein may be performed using an information handling system with computer- readable instructions that perform the recited method steps.
  • the methods disclosed herein may be implemented as a plug in to Petrel ® using the Ocean Application Programming Interface ("Ocean API") available from Schlumberger, Inc.
  • the methods and systems disclosed herein may be implemented in conjunction with other geological modelling software such as, for example, GOCAD ® or SKUA ® software available from Paradigm ® or the RMS ® software available from Emerson Process Management.
  • the methods and systems disclosed herein will improve system operation by providing for easy integration and compatibility of various child regions into a parent region while allowing the existing software to provide all other necessary functionalities as desired by the user.
  • a geological model developed in accordance with embodiments of the present disclosure may be utilized in analysis and development of a desired geological formation.
  • the geological model developed using the methods and systems disclosed herein may be used during the exploration and production of hydrocarbons.
  • the geological model developed may be used to identify regions of interest that contain hydrocarbons and/or determine the most efficient approach for production of hydrocarbons.
  • the geological models using the methods and systems disclosed herein may be utilized in various steps of performing subterranean operations such as, for example, when drilling a wellbore in the subterranean formation, during the steam injection process, when performing various wireline or logging operations and/or when performing any other operations necessary to remove hydrocarbons from a subterranean formation.
  • subterranean operations such as, for example, when drilling a wellbore in the subterranean formation, during the steam injection process, when performing various wireline or logging operations and/or when performing any other operations necessary to remove hydrocarbons from a subterranean formation.
  • a geological model developed in accordance with the methods and systems disclosed herein may be used to characterize the formation(s) being penetrated in order to perform the drilling operations efficiently.
  • the methods and systems disclosed herein may be used in conjunction with other analysis and/or operations relating to development of hydrocarbons or other materials from a geological formation.

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Abstract

L'invention concerne des procédés et des systèmes améliorés pour modéliser de manière efficace et précise des formations géologiques. Un modèle géologique d'une région d'intérêt comprend une région parent ayant une pluralité de régions enfant. Un modèle géologique de la région parent est conçu. L'une de la pluralité de régions enfant est extraite à partir de la région parent tout en maintenant une première relation parent-enfant entre la région enfant et la région parent. Le modèle géologique de la région enfant peut ensuite être affiné ou manipulé. Le modèle géologique de la région enfant est ensuite réintégré avec le modèle géologique de la région parent.
PCT/US2015/024546 2014-04-08 2015-04-06 Procédés et systèmes améliorés pour modéliser des formations géologiques WO2015157186A1 (fr)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
FR3051938A1 (fr) * 2016-05-31 2017-12-01 Ifp Energies Now Procede d'exploitation des hydrocarbures d'une formation souterraine, au moyen d'une mise a l'echelle optimisee

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090077100A1 (en) * 1996-08-22 2009-03-19 Hancock S Lee Unified geograhic database and methods of creating, maintaining and using the same
WO2009082563A1 (fr) * 2007-12-21 2009-07-02 Exxonmobil Upstream Research Company Procédé et appareil pour analyser des données tridimensionnelles
US20090248378A1 (en) * 2004-07-01 2009-10-01 Dachang Li Method For Geologic Modeling Through Hydrodynamics-Based Gridding (Hydro-Grids)
WO2010056427A1 (fr) * 2008-11-14 2010-05-20 Exxonmobil Upstream Research Company Formation d’un modèle d’une région du sous-sol
WO2011056347A1 (fr) * 2009-11-05 2011-05-12 Exxonmobil Upstream Research Company Procédé permettant de créer un modèle terrestre à strates hiérarchisées

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090077100A1 (en) * 1996-08-22 2009-03-19 Hancock S Lee Unified geograhic database and methods of creating, maintaining and using the same
US20090248378A1 (en) * 2004-07-01 2009-10-01 Dachang Li Method For Geologic Modeling Through Hydrodynamics-Based Gridding (Hydro-Grids)
WO2009082563A1 (fr) * 2007-12-21 2009-07-02 Exxonmobil Upstream Research Company Procédé et appareil pour analyser des données tridimensionnelles
WO2010056427A1 (fr) * 2008-11-14 2010-05-20 Exxonmobil Upstream Research Company Formation d’un modèle d’une région du sous-sol
WO2011056347A1 (fr) * 2009-11-05 2011-05-12 Exxonmobil Upstream Research Company Procédé permettant de créer un modèle terrestre à strates hiérarchisées

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
FR3051938A1 (fr) * 2016-05-31 2017-12-01 Ifp Energies Now Procede d'exploitation des hydrocarbures d'une formation souterraine, au moyen d'une mise a l'echelle optimisee
EP3252508A1 (fr) * 2016-05-31 2017-12-06 IFP Energies nouvelles Procede d'exploitation des hydrocarbures d'une formation souterraine, au moyen d'une mise a l'echelle optimisee

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